Credit rating of companies admitted to Tehran Stock Exchange

Document Type : Original Article

Authors

1 Corresponding author: Doctoral student of accounting department, Urmia branch, Islamic Azad University, Urmia, Iran

2 Associate Professor, Department of Accounting, Urmia Branch, Islamic Azad University, Urmia, Iran

3 Associate Professor, Department of Mathematics, Urmia Branch, Islamic Azad University, Urmia, Iran.: Email: Jafarian5594@yahoo.com

Abstract

The purpose of this research is credit rating of companies. For this purpose, in the first step, by studying the research literature and methodology of the top credit rating institutions, 10 variables that had the greatest impact on determining the credit quality of companies and are relevant in Iran's environment were used as the basis for credit rating. These variables are in two parts, quantitative and qualitative, all of which are quantified based on accounting information. Based on these variables, the information of 146 companies and 730 years of companies admitted to the stock exchange for the period of 2015 to 2019 were extracted for the credit rating of the companies from the data coverage super analysis model proposed by Hadi Vinche (2012). After that, the companies were placed in 9 categories using the K-means clustering method (Standard & Poor's 2021). To validate the model, the accuracy of the model was determined using the risk of default based on the leverage ratio of the total credit facility to the market value of the owners' rights. The result of the research included determining the credit rating of the investigated companies according to the selected indicators, each of which was assigned a rating from AAA to D. These ratings indicate the relative financial ability of companies to pay their debts on time. The closer the company's rating is to D, the lower the financial ability is, and the closer it is to AAA, the higher it is. The results show the selection of the influencing indicators in the credit rating and the correct determination of the credit rating.

Keywords


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ابراهیم سروعلیا،خان زاده،محمد،محقق نیا، محمد جواد،(1398). طراحی الگوی رتبه بندی اعتباری بانک های اسلامی ایران.نشریه تحقیقات مالی اسلامی
احمدی، موسی. (1396). نهاد مالی رتبه‌بندی اعتباری، الزامات و پیش‌نیازها. شرکت رتبه‌بندی اعتباری برهان
بلو، قاسم و احمدوند، میثم. (1399). الگویی برای پیش‌بینی نکول شرکتی در بورس اوراق بهادار تهران. مجله پژوهش­های تجربی حسابداری، 10(36)، ؟؟؟1- 38؟؟؟.
جهانشاهلو، غلامرضا؛ حسین‌زاده لطفی، فرهاد و نیکومرام، هاشم. (1387). تحلیل پوششی داده‌ها و کاربردهای ­آن. تهران: انتشارات دانشگاه آزاد اسلامی واحد علوم تحقیقات.
خان‌زاده، محمد؛ محقق‌نیا، محمدجواد و ابراهیمی سرو علیا، محمدحسن. (1398). الگوی طراحی الگوی رتبه‌بندی اعتباری بانک‌های اسلامی ­ایران. مجله تحقیقات مالی اسلامی، 10(17)، 1-40.
دیانتی ‌دیلمی،‌ زهرا. (1394‌). روش ‌تحقیق‌ در‌ حسابداری. شهرتهران: نشر‌ عدالت ‌نوین.
رضایی، علی؛ جهانشاد، آزیتا و تقی نتاج ملکشاه، حسین. (1398). شناسایی و رتبه‌بندی چالش‌های پیاده‌سازی مدل زیان اعتباری مورد انتظار در بانک‌های ایران با استفاده از تکنیک تحلیل سلسله‌مراتب فازی و ارائه راهکار به کمک روش واسپاس. فصلنامه بررسی‌های حسابداری و حسابرسی، 26(2)، ؟؟؟72-49؟؟؟.
قلی‌زاده، محمدحسن. (1383). طراحی مدل رتبه‌بندی شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران با استفاده از تحلیل پوششی داده‌ها. رساله دکتری، گروه آموزشی حسابداری، دانشکده اقتصاد ومدیریت،‌ دانشگاه تهران.
مدنی محمدی، ‌حمید. ‌(1385‌). تدوین­ مدل ­برای ­رتبه­بندی ­شرکت‌های­ کارگزاری­ بورس اوراق بهادار تهران. ‌فصلنامه ‌اندیشه‌ صادق، ‌23‌، 82-65.
مهرگان، محمدرضا. (1383). ارزیابی عملکرد سازمان‌ها: رویکردی کمی با استفاده از تحلیل پوششی داده‌ها. تهران: انتشارات دانشگاه تهران.
Abad,P., Alsakka, R., & Gwilym, O. (2018). The influence of rating levels and rating convergence on the spillover effects of sovereign credit actions. Journal of International Money and Finance, 85(C), 40???-57???.
Aysun, A. (2016). Structuralshifts increditrating standards. From www. Researchgate. Net.
Chikolwa, B. & Chan, F. (2008). Determinants of commercial mortgage-backed securities credit ratings: Australian evidence. International Journal of Strategic Property Management, Vol. 12, pp. 69–94.
Dorfleitner, G., Grebler, J., & Utz, S. (2020).The Impact of corporate social and environmental performance on credit rating prediction: North America versus Europe. Journal of Risk, 22(6), 1???-33???.
Ubarhande, P & Chandani, A. (2021). Elements of Credit Rating: A Hybrid Review and Future Research Agenda. From www. SSRN. Com
Hadi-Vencheh A & Esmaeilzadeh, A. (2013). A new super-efficiency model in the presence of negative data. Journal of the operational research society, 64, 396-401.
Halkos George, and Salamouris D. (2004). Efficiency measurement of the Greek commercial banks with the use of financial ratios: A Data Envelopment Analysis approach, Management Accounting Research, 15(2): 201-224
Hwang, Ruey-Ching; Chung, Huimin & Chu, C. K. (2010). Predicting issuer credit ratings using a semiparametric method. Journal of Empirical Finance, 17, 120-137.
Graham, J., C. Harvey & S. Rajgopal. (2005). The economic implications of corporate financial reporting. Journal of Accounting and Economics, 40, 3-73.
Gumparthi, Srinvas, Khatri, Swetha & Manickavasagam, V. (2011). Design and development of credit rating model for public sector bsanks in India. Journal of Accounting and Taxation, 3(5), 105-124.
Jiang, X & Packer, F. (2017). Credit ratings of domestic and global agencies: What drives the differences in China and how are they priced?. From www.bis.org.
Hájek, Petr. (2012). Credit rating analysis using adaptive fuzzy rule-based systems: an industry-specific approach. CEJOR, 20, 421-434.
Langohr, Herwig & Langohr, Patricia. (2009). The Rating Agencies and Their Credit Ratings: What They Are, How They Work, and Why They are Relevant. Wiley Finance Series.
Shankar, S. (2019). The Role of credit rating agencies in addressing gaps in micro and small enterprise financing: The Case of India. ADBI Working Paper. Tokyo: Asian Development Bank Institute.
Marcin, Tomasz. (2009). Application of Data Envelopment Analysis in Credit Scoring. Master’s Thesis in Financial Mathematics, Technical Report.
Mokhatab-Rafiei, Farimah & et al????????. (2012). MCDM-based model for predicting corporate credit rating:Some results for the Iran corporate sector 213 Interdisciplnary. Journal of contemporary research in business, 3(11), 589-596.